title: Parameterized SVM for personalized drug concentration prediction.
abstract: This paper proposes a parameterized Support Vector Machine (ParaSVM) approach for modeling the Drug Concentration to Time (DCT) curves. It combines the merits of Support Vector Machine (SVM) algorithm that considers various patient features and an analytical model that approximates the predicted DCT points and enables curve calibrations using occasional real Therapeutic Drug Monitoring (TDM) measurements. The RANSAC algorithm is applied to construct the parameter library for the relevant basis functions. We show an example of using ParaSVM to build DCT curves and then calibrate them by TDM measurements on imatinib case study.
authors.forename: Wenqi
authors.initials: W
authors.lastname: You
authors.forename: Alena
authors.initials: A
authors.lastname: Simalatsar
authors.forename: Giovanni
authors.initials: G
authors.lastname: De Micheli
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000378:metabolism / Q000627:therapeutic use; D001549:Benzamides / Q000378:metabolism / Q000627:therapeutic use; D016903:Drug Monitoring; D006801:Humans; D000068877:Imatinib Mesylate; D015464:Leukemia, Myelogenous, Chronic, BCR-ABL Positive / Q000188:drug therapy; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D010879:Piperazines / Q000378:metabolism / Q000627:therapeutic use; D057285:Precision Medicine; D011743:Pyrimidines / Q000378:metabolism / Q000627:therapeutic use; D060388:Support Vector Machine; D013997:Time Factors
chemical_list: D000970:Antineoplastic Agents; D001549:Benzamides; D004364:Pharmaceutical Preparations; D010879:Piperazines; D011743:Pyrimidines; D000068877:Imatinib Mesylate